code: def export_h2o(pipeline, pipeline_path, test_data):
      # Export it
      pipeline.save(path=pipeline_path)
  
      # Import pipeline
      json_path_load = create_correct_path(pipeline_path)
      new_pipeline = Pipeline.from_serialized(json_path_load)
  
      results = new_pipeline.predict(
          input_data=test_data, output_mode="full_probs"
      )
      prediction_after_export = results.predict[:, 0]
      print(f"After export {prediction_after_export[:4]}")
  
file_path: FEDOT/examples/advanced/automl/h2o_example.py
imports: import numpy
  from sklearn.metrics import roc_auc_score, mean_squared_error, mean_absolute_error
  from examples.advanced.time_series_forecasting.composing_pipelines import visualise
  from examples.simple.pipeline_import_export import create_correct_path
  from fedot.core.data.data_split import train_test_data_setup
  from fedot.core.pipelines.node import PipelineNode
  from fedot.core.pipelines.pipeline import Pipeline
  from fedot.core.repository.operation_types_repository import OperationTypesRepository
  from test.unit.tasks.test_classification import get_iris_data
  from test.unit.tasks.test_forecasting import get_ts_data
  from test.unit.tasks.test_regression import get_synthetic_regression_data, get_rmse_value
label_desc: Use image node_id 4 for calling a global function with example usage: export_h2o(pipeline, pipeline_path, test_data) without return types